# A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models

**Authors:** Haopeng Zhang, Philip S. Yu, Jiawei Zhang  
**arXiv:** [2406.11289](https://arxiv.org/abs/2406.11289)  
**Date:** 2024-06-17  
**Summary page:** https://www.summarizepaper.com/fr/paper/2406.11289v1/

## Abstract

Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed overview of datasets, evaluation metrics, and summarization methods before the LLM era, encompassing traditional statistical methods, deep learning approaches, and PLM fine-tuning techniques, and (2) the first detailed examination of recent advancements in benchmarking, modeling, and evaluating summarization in the LLM era. By synthesizing existing literature and presenting a cohesive overview, this survey also discusses research trends, open challenges, and proposes promising research directions in summarization, aiming to guide researchers through the evolving landscape of summarization research.